The idea behind peak-motifs is that we detect exceptional
words based on distinct and complementary criteria:

global over-representation (oligo-analysis and
dyad-analysis): a word/dyad is more frequent than expected from
the background model. The over-repressentation is tested with a
right-tailed binomial significance test.

positional bias (position-analysis): a word has a
heterogeneous of occurrences in the test sequences, i.e. there are
regions with higher frequency and other regions with lower
frequencies than the average of the same word observed over the
whole width of the sequences. Positional bias is tested with a
chi-squared tests.

local over-representation (local-word-analysis): the same
test as for oligo-analysis (significance of the right tail of the
binomial distribution) applied successfully to positional windows
defined over the test set.

For position-analysis and local-word-analysis, the sequences
are supposed to be aligned over some reference. For peaks, the
reference is the summit (or center) of each sequence.

NoPeak coordinates specified in fasta headers in bedtools getfasta format (also for retrieve-seq-bed output). Fasta headers should be in the form: >3:81458-81806(.)Peak coordinates specified in fasta headers of the test sequence file (Galaxy format) Fasta headers should be in the form: >mm9_chr1_3473041_3473370_+Peak coordinates provided as a custom BED file.The 4th column of the BED file (feature name) must correspond to the fasta headers of sequencesAssembly version (UCSC)

Reporting options

Plotting options

Reference position for position-analysis and sequence scanningOriginOffsetUse R to generate plots (only works for servers with R installed).

OutputdisplayemailNote: email output is preferred for very large datasets or many comparisons with motifs collections